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library(plotly)
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
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When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).
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names(df)
[1] "Release" "Release.date" "Release.type" "Band" "Genre"
[6] "Location" "Lyrical.themes" "Number.of.reviews" "Average.rating" "genre_early"
[11] "genre_later" "genre_early_secondary" "genre_later_secondary" "location_early" "genre_early_main"
[16] "genre_later_main" "genre_early_stripped" "genre_later_stripped"
df <- filter(!(genre_early_main %in% c('Rock', 'Other'))) %>%
mutate(release_year = as.numeric(str_sub(Release.date,1,4)))
Error in genre_early_main %in% c("Rock", "Other") :
object 'genre_early_main' not found
levels(df_year$genre_early_main)
[1] "Heavy Metal" "Doom Metal" "Thrash Metal" "Power Metal" "Nu Metal" "Progressive Metal"
[7] "Black Metal" "Metalcore" "Death Metal" "Folk Metal" "Ambient"
g1 <- ggplot(df_year, aes(x=release_year, y=percent, fill=fct_rev(genre_early_main))) +
geom_area(position = 'stack') +
labs(x="Release Year",
y = "Percent of Releases",
fill = "Main Genre",
title = "Percent of Metal Genre Releases By Year") +
#xlim(c(1970, 2018)) + ylim(c(0, 1)) +
scale_y_continuous(limits=c(0,1), labels = scales::percent, expand = c(0, 0)) +
scale_x_continuous(limits=c(1970, 2018), expand = c(0, 0)) +
scale_fill_manual(values = c("Black Metal" = "#000000", #black
"Death Metal" = "#8f0000", #dark red
"Thrash Metal" = "#7cf000", #light green
"Doom Metal" = "#7e3f0c", #brown
"Ambient" = "#7d7d7d", #gray
"Power Metal" = "#f72bad", #pink
"Heavy Metal" = "#1d00fa", #blue
"Metalcore" = "#ee6917",
"Nu Metal" = "#ffd60a",
"Progressive Metal" = "#0adeff", #light blue
"Folk Metal" = "#b120d9" #purple
)
) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
ggplotly(g1)
names(df)
[1] "Release" "Release.date" "Release.type" "Band" "Genre"
[6] "Location" "Lyrical.themes" "Number.of.reviews" "Average.rating" "genre_early"
[11] "genre_later" "genre_early_secondary" "genre_later_secondary" "location_early" "genre_early_main"
[16] "genre_later_main" "genre_early_stripped" "genre_later_stripped" "release_year"
g2 <- df %>% filter(!(genre_early_main %in% c('Rock', 'Other'))) %>%
mutate(release_year = as.numeric(str_sub(Release.date, 1, 4)),
Average.rating = round(Average.rating, 1)) %>%
filter(Number.of.reviews >= 5) %>%
ggplot() +
geom_point(aes(x = Number.of.reviews,
y = Average.rating,
color = genre_early_main,
text=sprintf("Band: %s
Release: %s
Release Date: %s
Genre: %s
Rating: %s
Number of Reviews: %s
Tags: %s",
Band, Release, Release.date, genre_early_main, Average.rating, Number.of.reviews, genre_early_stripped)),
position = position_jitter(width = 0.5, height = 0.5),
size = 0.4,
alpha = 0.6
) +
labs(x="Number of Reviews",
y = "Rating",
color = "Main Genre",
title = "Metal Releases By Rating and Number of Reviews") +
scale_color_manual(values = c("Black Metal" = "#000000", #black
"Death Metal" = "#8f0000", #dark red
"Thrash Metal" = "#7cf000", #light green
"Doom Metal" = "#7e3f0c", #brown
"Ambient" = "#7d7d7d", #gray
"Power Metal" = "#f72bad", #pink
"Heavy Metal" = "#1d00fa", #blue
"Metalcore" = "#ee6917",
"Nu Metal" = "#ffd60a",
"Progressive Metal" = "#0adeff", #light blue
"Folk Metal" = "#b120d9" #purple
)
) +
scale_y_continuous(limits=c(0,100), expand = c(0, 0)) +
scale_x_continuous(limits=c(5, 45), expand = c(0, 0)) +
theme_light() +
theme(#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
#panel.background = element_blank(),
axis.line = element_line(colour = "black"))
Ignoring unknown aesthetics: text
#geom_jitter()
ggplotly(g2, tooltip="text")
df %>% plot_ly(x = ~Number.of.reviews, y = ~Average.rating, name = "Band")
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 
```{r}
library(tidyverse)
library(plotly)
```

```{r}
df <- read.csv('albums_cleaned.csv', stringsAsFactors = FALSE)
head(df)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r}
names(df)
```
```{r}
library(lubridate)
df <- df %>% filter(!(genre_early_main %in% c('Rock', 'Other'))) %>% 
  mutate(release_year = as.numeric(str_sub(Release.date,1,4)))

df_year <- df %>% 
  filter(!(genre_early_main %in% c('Rock', 'Other'))) %>%
  mutate(release_year = str_sub(Release.date,1,4), 
         release_year_month = str_sub(Release.date,1,7)) %>% 
    mutate(release_year = as.numeric(release_year)) %>% 
  filter(!is.na(genre_early_main), genre_early_main != '', release_year >= 1970, release_year < 2019) %>%
  group_by(release_year) %>% 
  count(genre_early_main) %>%
  mutate(percent = n/sum(n)) %>% 
  ungroup() %>% 
  mutate(genre_early_main = fct_reorder(genre_early_main, release_year))
  #summarize(a = count(genre_early_main)/total)# %>%
  #count(genre_early_main)


df_year %>% arrange(release_year)
```
```{r}
levels(df_year$genre_early_main)
```

```{r fig.height=5, fig.width=8}
g1 <- ggplot(df_year, aes(x=release_year, y=percent, fill=fct_rev(genre_early_main))) + 
    geom_area(position = 'stack') + 
  labs(x="Release Year", 
       y = "Percent of Releases", 
       fill = "Main Genre",
       title = "Percent of Metal Genre Releases By Year") + 
  #xlim(c(1970, 2018)) + ylim(c(0, 1)) + 
    scale_y_continuous(limits=c(0,1), labels = scales::percent, expand = c(0, 0)) + 
  scale_x_continuous(limits=c(1970, 2018), expand = c(0, 0)) + 
  scale_fill_manual(values = c("Black Metal" = "#000000", #black
                                "Death Metal" = "#8f0000", #dark red
                               "Thrash Metal" = "#7cf000", #light green
                               "Doom Metal" = "#7e3f0c", #brown
                               "Ambient" = "#7d7d7d", #gray
                               "Power Metal" = "#f72bad", #pink
                               "Heavy Metal" = "#1d00fa", #blue
                               "Metalcore" = "#ee6917",
                               "Nu Metal" = "#ffd60a",
                               "Progressive Metal" = "#0adeff", #light blue
                               "Folk Metal" = "#b120d9" #purple
                               )
                     ) + 
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  panel.background = element_blank(), axis.line = element_line(colour = "black"))

ggplotly(g1)
```
```{r}
names(df)
```

```{r fig.height=5, fig.width=5}
g2 <- df %>% filter(!(genre_early_main %in% c('Rock', 'Other'))) %>%
  mutate(release_year = as.numeric(str_sub(Release.date, 1, 4)),
         Average.rating = round(Average.rating, 1)) %>% 
  filter(Number.of.reviews >= 5) %>%
  ggplot() + 
  geom_point(aes(x = Number.of.reviews, 
                 y = Average.rating,
                 color = genre_early_main,
                 text=sprintf("Band: %s
                              Album: %s
                              Release Date: %s
                              Genre: %s
                              Rating: %s
                              Number of Reviews: %s
                              Tags: %s", 
                              Band, Release, Release.date, genre_early_main, Average.rating, Number.of.reviews, genre_early_stripped)), 
             position = position_jitter(width = 0.5, height = 0.5),
             size = 0.4,
             alpha = 0.6
             ) + 
  labs(x="Number of Reviews", 
       y = "Rating", 
       color = "Main Genre",
       title = "Metal Releases By Rating and Number of Reviews") + 
    scale_color_manual(values = c("Black Metal" = "#000000", #black
                                "Death Metal" = "#8f0000", #dark red
                               "Thrash Metal" = "#7cf000", #light green
                               "Doom Metal" = "#7e3f0c", #brown
                               "Ambient" = "#7d7d7d", #gray
                               "Power Metal" = "#f72bad", #pink
                               "Heavy Metal" = "#1d00fa", #blue
                               "Metalcore" = "#ee6917",
                               "Nu Metal" = "#ffd60a",
                               "Progressive Metal" = "#0adeff", #light blue
                               "Folk Metal" = "#b120d9" #purple
                               )
                     ) + 
    scale_y_continuous(limits=c(0,100),  expand = c(0, 0)) + 
  scale_x_continuous(limits=c(5, 45), expand = c(0, 0)) + 
  theme_light() + 
  theme(#panel.grid.major = element_blank(), 
        #panel.grid.minor = element_blank(),
        #panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))
  #geom_jitter()
ggplotly(g2, tooltip="text")

```
```{r}
df
df %>% 
  plot_ly(x = ~Number.of.reviews, y = ~Average.rating) %>%
  add_markers()
```




```{r fig.width=5}
df_year <- df_year %>% ungroup() %>% mutate(release_year = as.numeric(release_year)) %>% filter(release_year < 2019, release_year >= 1969)
df_year <- df_year %>% mutate(genre_early_main = fct_reorder(genre_early_main, release_year))
df_year <- df_year %>% filter(!is.na(genre_early_main), n>0)
ggplot(df_year, aes(x=release_year, y=percent, fill=genre_early_main)) + 
    geom_area(position = 'stack')
```

```{r}
df_year_month <- df %>% mutate(release_year = str_sub(Release.date,1,4), 
                               release_year_month = str_sub(Release.date,1,7)
                               ) %>% 
  group_by(release_year_month) %>% 
  count(genre_early_main) %>% 
  mutate(percent = n/sum(n))# %>% 
  #ungroup() %>% 
  #mutate(release_year_month = lubridate::as_datetime(release_year_month)) %>% 
  #mutate(genre_early_main = fct_reorder(genre_early_main, release_year_month))


df_year_month %>% 
  ggplot(aes(x=release_year_month, y=percent, fill=genre_early_main)) + 
  geom_area(position='stack')

df_year_month
```

